To learn an automated classification system, we leverage deep transfer learning to learn a classifier that can successfully identify snow leopards with high precision and recall. We improve the classifier through the use of dataset augmentation and ensembling leopard probabilities over bursts of images. The simplest model uses features from ResNet50, trained on the ImageNet corpus. ResNet50 is a state of the art network that combines convolutional and recurrent components to capitalize on the spatial regularity of natural images and effectively learn the optimal depth of the network [1]}. We truncate the last, image-net specific layer(s) but keep a majority of the early layers. This lets us leverage the domain-general low to mid level features and adapt the network to the new classification task. To adapt the network, we append and train a SparkML logistic regressor that maps the features to a binary label. This effectively adds an additional fully connected layer to the truncated network. A schematic overview of the algorithm is provided in Figure REF . We refer to these algorithms as “RN1” and “RN2” for ResNet50 with 1 and 2 truncated layers respectively.
